Cancer is a devastating disease affecting more than 100 million people, globally. It is the uncontrolled growth of cells that eventually can spread throughout the body. The transformation of normal cells into cancer cells is still not well understood, although several hallmarks of changes need to be acquired. A better understanding of the molecular mechanisms that lead to cancer is essential to improve the diagnosis, prevention, and curation. But, this is hampered by the complexity of several steps involved. Large international efforts like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are acquiring genomic data that give a broad spectrum on the changes that cancer cells undergo, like copy number alterations, methylation profiles or over-active genes. The combined analysis of such data is, however, not trivial, and DBL investigates novel ways to do so, based on advances in machine learning. Alternatively, recent advances in cancer therapies have shown that combination of drugs (drug cocktails) seem to be particular effective. Combined analysis methodologies can also be used to predict effects of combination drugs to accelerate the search for effective therapies. In its cancer research, DBL closely collaborates with the Dutch Cancer Institute (NKI).
- Integrating omics data
- Predicting effects of drug on treating cancer
- Translating cell line data to human tissue data
- Estimating copy number variations